383 research outputs found
Schinortriterpenoids from Tujia ethnomedicine Xuetong-The stems of Kadsura heteroclita
No full textCao, Liang, Shehla, Nuzhat, Li, Bin, Jian, Yuqing, Peng, Caiyun, Sheng, Wenbing, Liu, Leping, Cai, Xiong, Man, Rongyong, Liao, Duan-Fang, Choudhary, M. Iqbal, Rahman, Atta-ur, Wang, Wei (2020): Schinortriterpenoids from Tujia ethnomedicine Xuetong-The stems of Kadsura heteroclita. Phytochemistry (112178) 169: 1-7, DOI: 10.1016/j.phytochem.2019.112178, URL: http://dx.doi.org/10.1016/j.phytochem.2019.11217
Fig. 5. X in Schinortriterpenoids from Tujia ethnomedicine Xuetong-The stems of Kadsura heteroclita
No full textFig. 5. X-ray crystallographic structure of 1.Published as part of Cao, Liang, Shehla, Nuzhat, Li, Bin, Jian, Yuqing, Peng, Caiyun, Sheng, Wenbing, Liu, Leping, Cai, Xiong, Man, Rongyong, Liao, Duan-Fang, Choudhary, M. Iqbal, Rahman, Atta-ur & Wang, Wei, 2020, Schinortriterpenoids from Tujia ethnomedicine Xuetong-The stems of Kadsura heteroclita, pp. 1-7 in Phytochemistry (112178) 169 on page 3, DOI: 10.1016/j.phytochem.2019.112178, http://zenodo.org/record/829366
Fig. 7. Key 1H–1H in Schinortriterpenoids from Tujia ethnomedicine Xuetong-The stems of Kadsura heteroclita
No full textFig. 7. Key 1H–1H COSY (bold ─), and HMBC (→) correlations of compound 3, 4, 5, 6.Published as part of Cao, Liang, Shehla, Nuzhat, Li, Bin, Jian, Yuqing, Peng, Caiyun, Sheng, Wenbing, Liu, Leping, Cai, Xiong, Man, Rongyong, Liao, Duan-Fang, Choudhary, M. Iqbal, Rahman, Atta-ur & Wang, Wei, 2020, Schinortriterpenoids from Tujia ethnomedicine Xuetong-The stems of Kadsura heteroclita, pp. 1-7 in Phytochemistry (112178) 169 on page 4, DOI: 10.1016/j.phytochem.2019.112178, http://zenodo.org/record/829366
Fig. 6 in Schinortriterpenoids from Tujia ethnomedicine Xuetong-The stems of Kadsura heteroclita
No full textFig. 6. Experimental ECD spectra of compounds 4 (black), and 7 (red) in MeOH. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)Published as part of Cao, Liang, Shehla, Nuzhat, Li, Bin, Jian, Yuqing, Peng, Caiyun, Sheng, Wenbing, Liu, Leping, Cai, Xiong, Man, Rongyong, Liao, Duan-Fang, Choudhary, M. Iqbal, Rahman, Atta-ur & Wang, Wei, 2020, Schinortriterpenoids from Tujia ethnomedicine Xuetong-The stems of Kadsura heteroclita, pp. 1-7 in Phytochemistry (112178) 169 on page 4, DOI: 10.1016/j.phytochem.2019.112178, http://zenodo.org/record/829366
Quantitative Content Analysis Methods in Instructional Technology Research: Defining, Coding, Analyzing and Modeling (DCAM)
No full textContent analysis has been applied in the research of instructional design and technology to analyze (a) text-based contents, such as online discussions, social media communications, or published articles, and (b) other formats of contents such as videos, audios, or pictures. The purpose of this article is to introduce a method of DCAM (Defining, Coding, Analyzing and Modeling) for content analysis with practice examples. DCAM is a quantitative method generated from a series of studies in instructional design conducted by the author, and supported by the literature in the field. The variables defined from the text-content or other formats of contents can be design related variables, learning related variables, micro-activities in learning, or behavior-performance related learning outcome. In this article, first, nominal, ordinal and scaled coding methods on those variables are demonstrated. Second, reliability measures in content-variable coding are reviewed and explored. Third, parametric and nonparametric statistics methods to examine those variables for content analysis are presented. Finally, some cautions and suggestions to conduct content analysis is discussed
An Examination of Student Outcomes and Student Satisfaction in a Flipped Learning Environment: A Quasi-Experimental Design
No full textFlipped learning has become a hot topic in education, in part because of the media portrayal of flipped learning in existing news stories. Although there has been a rise in popularity and implementation, there has been a lack of empirical research in the field of flipped learning. The purpose of this exploratory study was to address some of the issues related to previous research regarding student outcomes and satisfaction, gender differences, and lack of research in sociology courses. A two-by-two quasi-experimental design was established. Results were grouped by Learning Environment, which was expressed as the traditional learning group and the flipped learning group; and Gender was categorized as female and male. The Dependent Variables consisted of the Pretest, Posttest, Stratification Quiz, Sex/Gender Quiz, Race/Ethnicity Quiz, Higher-Ordered Unit Exam and student Satisfaction Scores. There were 111 participants in the current study. A two-way MANOVA was conducted to determine if any interaction effects or main effects existed based on the Dependent Variables. The interaction effect of Learning Environment by Gender was examined before inspecting the individual main effects. The main effect for Gender based on Dependent Variables was reviewed. The interaction effect (Learning Environment by Gender) and main effect for Gender on the combined Dependent Variables was not significant. The main effect for Learning Environment based on the combined Dependent Variables was significant (V = .238, F (6, 90) = 4.692, p < .001, η2p = .238). The results from the quantitative analysis and qualitative findings mirrored previous literature
Minigene splicing assays reveal new insights into exonic variants of the SLC12A3 gene in Gitelman syndrome
No full textBackground: Gitelman syndrome (GS) is a type of salt-losing tubular disease, most of which is caused by SLC12A3 gene variants, and missense variants account for the majority. Recently, the phenomenon of exon skipping, in which variants disrupt normal pre-mRNA splicing, has been related to a variety of diseases. Therefore, we hypothesize that a certain proportion of SLC12A3 variants can result in disease via interfering with the normal splicing process. Methods: We analyzed 342 previously presumed SLC12A3 missense variants using bioinformatics programs and identified candidate variants that may alter the splicing of pre-mRNA through minigene assays. Results: Our study revealed that, among ten candidate variants, six variants (c.602G>A, c.602G>T, c.1667C>T, c.1925G>A, c.2548G>C, and c.2549G>C) led to complete or incomplete exon skipping by affecting exonic splicing regulatory elements and/or disturbing canonical splice sites. Conclusion: It is worth mentioning that this is the largest study on pre-mRNA splicing of SLC12A3 exonic variants. In addition, our study emphasizes the importance of detecting splicing function at the mRNA level in GS and indicates that minigene analysis is a valuable tool for splicing functional assays of variants in vitro
A Cross Sample Analysis
No full textThere are different methods to examine the predictive validity of an instrument. In this chapter, the author presents a method of validation—cross sample analysis, using a study as an example. This study demonstrates the procedures to determine whether a technology attitude instrument can predict student technology learning achievement consistently across four featured samples, with the data from two universities over a nine-year period. A base-model of prediction is first developed and then tested. The predictive validity of the instrument is confirmed by the model testing results that no significant differences exist between the means of the predicted and observed learning achievement scores in each featured sample group. Background knowledge and other relevant methods of validation are also reviewed in this chapter.</jats:p
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